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Invaris AgentSec is an open-source framework for testing AI agents against adversarial inputs and unsafe behavior before deployment. Rather than checking only whether an agent answers a prompt correctly, it tests how the agent handles tool use, memory, retrieved content, permissions, and limits across a workflow. Its central security question is whether an agent knows when not to act.
What is Invaris AgentSec?
AgentSec is a developer-facing adversarial security and reliability testing framework from Invaris Labs. Its purpose is to make agent testing repeatable, in a way that fits ordinary development workflows. Developers configure an agent endpoint and a policy, run test scenarios, and inspect reports for behavior that violates that policy.
The distinction from a simple prompt check matters. An autonomous agent may interpret untrusted text, choose among tools, retain information in memory, and decide what to do next. A response can look harmless while the surrounding workflow exposes a secret, invokes a tool the task did not permit, or takes an action the user never authorized. AgentSec is aimed at testing those interactions, not just the wording of a final answer.
The project’s framing is: “Don’t just test whether your AI agent can complete a task. Test whether it knows when not to act.” The line is attributed to Arunima Chaudhuri, founder of Invaris Labs and author of the article introducing the tool.
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Why test an agent’s workflow instead of its answers?
For an agent, security depends on what it can do as well as what it says. A prompt injection or poisoned tool response can try to steer the system toward confidential data, an unauthorized tool call, or an irreversible action. A weakness may also emerge only after several steps—for example, when information obtained from one tool is sent to another destination.
That creates a different testing problem from checking a single model response. A useful test needs to consider the adversarial content, the agent’s decisions, the tools it invokes, and whether those actions comply with the task’s authorization and operational limits. AgentSec’s documented scenarios address this broader workflow-level problem.
Security is not the only concern. An agent can repeatedly call tools, consume more tokens or money than intended, or fail to stop. A test policy that sets execution limits can make these operational risks part of the evaluation alongside unauthorized actions and data exposure.
What risks does AgentSec test?
AgentSec documents coverage across common agent failure modes, including attacks delivered through tools and retrieved content. Its findings are mapped to the OWASP Top 10 for Agentic Applications, identified in the project documentation as ASI01–ASI10.
Prompt injection and untrusted content
The suite covers direct and indirect prompt injection. Direct injection targets an agent through an instruction presented to it; indirect injection can arrive inside material the agent is asked to process, such as a retrieved document. The documented cases also include hostile content delivered through email and browser surfaces, so the test target is not limited to text entered directly by a user.
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Tools, MCP, and unauthorized actions
Tests cover unauthorized tool invocation and actions that the current task did not authorize. AgentSec also addresses tool-output poisoning and malicious MCP server descriptions or responses. Its MCP scanner checks tool descriptions and definitions for issues including hidden characters, shadowing, homoglyph names, misleading annotations, and changed definitions.
These checks matter because an agent may rely on tool names and descriptions when deciding what to call. A misleading or altered definition can affect that decision even when the user’s request appears ordinary. The scanner focuses on these MCP-related signals; the documentation does not establish that it can prove a server or tool is safe in every respect.
Secrets, memory, and persistence
The documented suite checks for secret and sensitive-data leakage, as well as memory poisoning and unsafe persistence. These risks concern both what an agent reveals during a run and what hostile instructions or information might influence later behavior if retained.
Runaway use and missing stop conditions
AgentSec includes checks for excessive tool calls, token usage, and cost, along with missing termination conditions. This makes it possible to test whether a workflow stays within configured limits instead of treating successful task completion as the only outcome that matters.
Identity, delegation, and risky data flows
Other documented areas include identity, session, and authorization confusion; multi-agent delegation abuse; and privilege escalation through a confused deputy. The suite also covers dangerous compositions in which data read by one tool is sent to another destination without authorization. These scenarios help surface failures that may not be visible when each tool is assessed in isolation.
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Behavior changes between versions
AgentSec documents behavioral regression testing across models and prompts. A change to a model or prompt can alter how an agent handles the same workflow, so previously identified cases can be useful checks when a system changes.
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How do you run an AgentSec test?
The documented basic workflow is to install the command-line interface, define an agentsec.yaml policy, and run agentsec test. The policy describes the agent endpoint, allowed tools, forbidden actions, execution limits, and test categories. The exact configuration syntax and values should be taken from the project’s own documentation rather than inferred from this overview.
- Install the CLI. Run
pip install invaris-agentsecin the Python environment you intend to use for testing. - Define the policy. Create
agentsec.yamlwith the endpoint and the policy details AgentSec documents, including allowed tools, forbidden actions, step, tool, and cost limits, and the test categories to run. - Run the test command. From the environment and project context where the policy is available, run
agentsec testto execute scenarios and generate reports. - Review findings. Inspect the generated output for the scenario, observed behavior, and policy violation, then investigate whether the behavior reflects a real weakness in your agent’s workflow.
The available data identifies JSON and HTML as default report formats, with Markdown and SARIF options also documented. It does not provide a complete command reference, example YAML, or output schema here, so do not assume particular flags, field names, or report details beyond what the project documents.
How can you make the results useful in development?
A security test is more valuable when a team can repeat it after a change and compare the result. AgentSec documents several workflow features intended to support that practice.
- Deterministic seeds: use the documented seed capability to make scenario execution more repeatable.
- Replay: replay prior findings so a known failure can be checked again after a change.
- Report comparison: compare older and newer reports to look for regressions.
- CI integration: connect tests to GitHub Actions so checks can be included in a development pipeline.
- SARIF output: use the documented SARIF option when that format fits the team’s security-results workflow.
- Local dashboard: use the documented dashboard to review results locally.
These features answer different needs: replay helps reproduce a known case, while comparing reports helps identify behavior that changed between runs. A CI job can make testing more routine, but it cannot by itself decide whether a finding is exploitable or whether the policy correctly represents the user’s authorization boundaries. A team still needs to review findings and maintain realistic policies.
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How should you design agent-security policies?
A policy is only useful if it reflects what the agent is genuinely allowed to do. Start with the task boundary: which tools may be used, what actions are forbidden, and what limits should apply to the run. Keep the authorization specific enough to distinguish a permitted action from a similar action that was never requested.
For example, an agent might be allowed to read a document for a task without being allowed to forward its contents elsewhere. That distinction corresponds to AgentSec’s documented concern about data read by one tool being sent to another destination without authorization. Likewise, a tool being technically available to an agent does not automatically mean every task permits its use.
Set execution limits with the agent’s intended work in mind. Limits on steps, tool calls, and cost help define when activity has gone beyond the expected run. If the agent legitimately needs more room for a task, the policy should reflect that requirement deliberately rather than leaving resource use unconstrained.
Finally, test the policy against changes in prompts and models. AgentSec documents regression checks across both; repeating prior scenarios after either changes can expose a behavior shift that a fresh, ordinary task check might miss.
What does AgentSec establish—and what does it not?
AgentSec’s documented capabilities make it a framework for exercising agent workflows against specified adversarial scenarios and policies. They do not, on their own, demonstrate that an agent is secure, that every attack will be detected, or that a particular deployment has passed an independent audit.
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No independently measured detection-rate or production-adoption benchmark is published in the sources described for this tool. Sample scenario and finding counts shown in project documentation are illustrative examples, not measured efficacy statistics. Treat reported findings as evidence to investigate, and a clean run as limited to the scenarios and policy you actually tested.
The documentation describes CLI use, policy-driven tests, report formats, replay, comparisons, CI integration, a dashboard, and MCP scanning. It does not establish a comparative performance advantage over other products, broad framework compatibility, or universal coverage. Those claims would require evidence beyond the capabilities listed here.
When is AgentSec a useful fit?
AgentSec is relevant when you are developing an agent that uses tools, reads untrusted content, retains memory, or delegates work, and you want repeatable checks for unsafe behavior. It is especially aligned with teams that want to test task authorization and tool interactions rather than relying only on isolated prompt evaluation.
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Frequently Asked Questions
Does AgentSec prove that my AI agent is secure if every test passes?
No. A passing run only reflects the scenarios and policy used for that run; it is not an independent audit or proof that untested attacks are impossible. The sources described for the project do not publish an independently measured detection rate.
Can AgentSec test an agent built with any framework?
The available documentation summary does not establish universal framework compatibility or list a complete set of supported adapters. Check the project documentation for the framework and integration details relevant to your implementation.
Does AgentSec send my agent traces or test data to a hosted service?
The available description does not establish how telemetry or test data is handled in every configuration. Review the project’s current privacy and deployment documentation before using sensitive inputs.
Is AgentSec an MCP server certification tool?
No certification capability is established in the available documentation. The described MCP scanner checks for specific issues in tool descriptions and definitions, which is narrower than certifying a server as safe.
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